1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2023
Spectral operator learning for parametric PDEs without data reliance
Junho Choi, Taehyun Yun, Namjung Kim +1
In this paper, we introduce the Spectral Coefficient Learning via Operator Network (SCLON), a novel operator learning-based approach for solving parametric partial differential equ…
math.NA2022★ 1 cited
Semi-analytic PINN methods for singularly perturbed boundary value problems
Gung-Min Gie, Youngjoon Hong, Chang-Yeol Jung
We propose a new semi-analytic physics informed neural network (PINN) to solve singularly perturbed boundary value problems. The PINN is a scientific machine learning framework tha…
cs.LG2022
Unsupervised Legendre-Galerkin Neural Network for Singularly Perturbed Partial Differential Equations
Junho Choi, Namjung Kim, Youngjoon Hong
Machine learning methods have been lately used to solve partial differential equations (PDEs) and dynamical systems. These approaches have been developed into a novel research fiel…